Latency reducing techniques for control channel decoding

ABSTRACT

Introduced here are techniques for reducing the latency at a terminal device. In particular, for reducing the latency of detecting the downlink control information (DCI) in a downlink signal such as the physical downlink control channel (PDCCH) signal. The techniques include determining a probability of each aggregation level of the control channel elements (CCE) in a signal carrying the DCI. The probability can be based on the packet error rate (PER) of the signal. The PER can vary based on factors such as the signal to noise ratio (SNR) and/or the fading measurements. Further, the probability can depend on the reception history of the terminal device. Based on these factors, the terminal device can decode the CCEs in an order indicative of the probability of the corresponding aggregation levels carrying the DCI.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application is a continuation of International ApplicationNo. PCT/US2021/017502 filed on Feb. 10, 2021, which claims priority toU.S. Provisional Patent Application No. 63/060,465 filed on Aug. 3,2020, entitled “METHOD FOR BLIND DECODING OF CONTROL CHANNEL,” both ofwhich are herein incorporated by reference in their entireties.

BACKGROUND

In wireless communication systems, the communications are generallycoded prior to transmission. The data in coded formats with headerinformation that can help the recipient decode the data upon receipt.The recipient (e.g., a terminal device), then, must be aware of wherethe information needed to decode the coded values is within the signal.The information needed for decoding is called the downlink controlinformation (DCI), which is conveyed in PDCCH (Physical Downlink ControlChannel) channel. Further, the DCI can be transmitted on multiplecontrol channel elements (CCEs) within a signal. The recipient of thesignal needs to parse the CCEs to determine the location of the DCIprior to decoding the CCEs.

Traditionally, the method used to find the information is a brute-forceapproach. More specifically, the recipient can search one-by-one in afirst come-first search basis. In other words, the recipient decodes andreads each resource block, in a linear fashion, until the DCI is found.

SUMMARY

The disclosed techniques relate to latency reducing techniques for anetwork device, and more particularly, to latency reducing techniquesfor decoding a control channel at a network device.

A first aspect of the present application provides a method for reducinglatency. The method includes: a terminal receives a physical downlinkcontrol channel (PDCCH) signal, the PDCCH signal including a pluralityof groups of control channel elements (CCEs), herein each group of CCEsis associated with an aggregation level of a plurality of aggregationlevels; the terminal determines a probability of downlink controlinformation (DCI) being present at each aggregation level of theplurality of aggregation levels based on a packet error rate (PER); andthe terminal decodes the groups of CCEs in an order, herein the order isbased on the probability of the DCI being present at each aggregationlevel.

A second aspect of the present application provides a method forreducing latency. The method includes: a network node transmits aphysical downlink control channel (PDCCH) signal, the PDCCH signalincluding a plurality of groups of control channel elements (CCEs),herein each group of CCEs is associated with an aggregation level of aplurality of aggregation levels; the network node causes determinationof a probability of downlink control information (DCI) being present ateach aggregation level of the plurality of aggregation levels based on apacket error rate (PER); and the network node causes decodes of thegroups of CCEs in an order, herein the order is based on the probabilityof the DCI being present at each aggregation level.

A third aspect of the present application provides a system. The systemincludes a receiver and a processor. The receiver is configured toreceive a physical downlink control channel (PDCCH) signal, the PDCCHsignal including a plurality of groups of control channel elements(CCEs), herein each group of CCEs is associated with an aggregationlevel of a plurality of aggregation levels. The processor is configuredto determine, based on a packet error rate (PER), a probability ofdownlink control information (DCI) being present at each aggregationlevel of the plurality of aggregation levels; and decode the groups ofCCEs in an order, herein the order is based on the probability of theDCI being present at each aggregation level.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments are illustrated by way of examples and are notintended to be limited by the figures in the accompanying drawings.

FIG. 1 illustrates a high level block diagram of the components of areceiver.

FIG. 2A illustrates an example of control channel elements (CCEs) beingreceived during a time period.

FIG. 2B illustrates an example of the order that the CCEs are decoded.

FIG. 2C illustrates another example of the order that the CCEs aredecoded.

FIG. 3 is a flowchart that illustrates a method for decoding CCEs.

FIG. 4 is a block diagram illustrating a diagrammatic representation ofa machine in the example form of a computer system operable to performaspects of the disclosed technology.

DETAILED DESCRIPTION

Telecommunication systems require recipients of signals to decode thevalues within the signal. Each signal can carry multiple types of datasuch as control data and user data. Due to this, the recipient of thesignal has to decode each signal to determine the pieces of the datathat apply to certain situations. For instance, certain data mightprovide instructions to the recipient on how to decode the remainingdata. Other data may provide details regarding the sequence at which therecipient should read the data. As such, decoding the signal is aprimary task of a recipient.

In particular, in the downlink direction, a terminal device needs todecode a received signal. To do so, a terminal device needs to parsethrough the signal to find the downlink control information (DCI) thatis needed to decode the signal. The DCI can be stored in multipleresource blocks that are aggregated into control channel elements(CCEs). An aggregation level is the number of CCEs used for sendingcontrol information such as DCI. The aggregation level often has valuessuch as 1, 2, 4, and 8.

DCI can also be used by a network to, for example, instruct a terminalto perform an uplink transmission, or adjust timing or power of acommunication. Therefore, due to the variability of instructions, thebit length of information in a DCI is also variable. Another factor isthe signal to noise ratio (SNR). The SNR of the received signal at aterminal can be low or high. When SNR of the received signal at aterminal is high, the network needs fewer resources (e.g., CCE with asmaller aggregation level) to transmit DCI. When the SNR is low, thenetwork needs more resources (e.g., CCE with a larger aggregation level)to transmit DCI.

Generally, the resource range for a network to send the DCI is large.Although the terminal, usually, knows the DCI range, the terminal doesnot know whether the network sends the DCI or not, in which specificresources the DCI is sent, and which DCI format the network sends theDCI, if at all. As mentioned above, the common approach is a linearsearch of CCEs with all possible sizes as they are received. In otherwords, a terminal device searches one-by-one until the CCEs with the DCIis found. This linear search causes multiple issues. A few of themultiple issues are described below.

First, the linear search technique uses more power than is necessary.Due to the nature of a linear search, there are situations where the DCIis found in the first hypothesis (with certain combination of CCEs) thatis decoded. However, there are also situations where the DCI is found inthe last hypothesis (with another combination of CCEs) that is decoded.As one can imagine, the inherent nature of the linear search leads tothe assumption that, on average, the DCI is found in the hypothesis thatis in the middle of the first and last hypothesis. In any case, thepower consumption also varies with where the DCI is found. Given thatpower consumption is a primary factor in the design of terminal device,the power consumption for finding the DCI must also be optimized.

Second, complementary to the power usage problem, is the usage ofcomputing resources to find the DCI. A terminal device has limitedcapacity and computing capability, and thus, the limited resources needto be used efficiently. In this case, the linear search for the DCI usesthe limited capabilities, which can be applied to other tasks. In otherwords, a terminal device may need allot computing capabilities forfinding the DCI irrespective of whether the DCI is found in the firsthypothesis or last hypothesis because the terminal device needs to allotresources for the worst-case scenario. Due to this, the allottedcomputed resources can be more efficiently used.

Third, the linear search causes latency; in particular, DCI detectionlatency. This is especially problematic in situations where the terminaldevice is communicating on multiple carriers. Large DCI detectionlatency leads to other consequences such as larger buffer sizes. Largerbuffer size requirements, in turn, leads to more hardware space beingused. Further, DCI detection latency leads to a poor user experience.For instance, in an autonomous driving car, the car is expected toprocess information and directions in near-real-time. If there is DCIlatency, the car may not process information as quickly as it should,which may lead to emergency situations. As such, the linear search cancause issues in the design of the terminal device and in application.

Introduced here, therefore, is at least one technique for moreefficiently detecting the DCI. In particular, the technique includesdetermining a probability that a DCI is transmitted at a givenaggregation level. The probability is dependent on the packet error rate(PER). The PER can depend on factors such as the signal to noise ratio(SNR) and fading measurements (e.g., fading channel). In addition to thePER, the probability can depend on the reception history of the terminaldevice. For instance, the logic behind technique can be based on theprinciple that distance from the base station effects the PER and thus,the probability of DCI being transmitted at each aggregation level. Ifthe terminal device is close to the base station, the SNR is likely tobe high. Due to this, the DCI is more likely to be transmitted at loweraggregation levels. This is because, at low SNR values, the base stationcan send DCI without having to send multiple copies to accommodate fornoise. Similar logic can be applied to the fading measurements. Fadingmeasurements can be affected by factors such as the Doppler shift andfrequency selectivity. For example, if a PDCCH is transmitted with ahigh error rate due to Doppler shift, the terminal can determine that itis more likely that the DCI is transmitted with a high aggregationlevel. Lastly, past reception history can help determine the probabilitythat a base station transmits DCI at a particular aggregation level. Forinstance, the reception history may indicate that a majority of the DCIwas previously transmitted at aggregation level 4. Thus, the terminalcan determine that CCE with aggregation level 4 are more likely to becarrying the DCI, than other CCEs. In this manner, the techniqueaddresses the issues described above and others by efficientlydetermining where the DCI is located.

In following description, the examples of a terminal device (e.g.,mobile device) and receiver are used, for illustrative purposes only, toexplain various aspects of the techniques. For example, a cellular phonecan apply a technique for locating the DCI. Note, however, thetechniques disclosed here are not limited to applicability to terminaldevices, receivers, or to any other particular kind of devices. Otherdevice, for example, electronic device or systems (e.g., base stations)may adapt the techniques in a similar manner.

Further, references are made to downlink directions and downlink controlinformation (DCI). These references are also made for illustrativepurposes only. As such, note that the techniques described herein can beapplied in other directions (e.g., uplink), and for locating otherinformation within a signal.

Control Channel Decoding Overview

FIG. 1 illustrates a high level block diagram 100 of the components of areceiver. Diagram 100 includes LLR buffer 102, de-rate matcher 104,decoder 106, error detector 108, blinding decoding controller 110, andaggregation level predictor 112. The output from these components andthe techniques that are applied between the components results in theDCI output to another component of the receiver (e.g., terminal device).

In some embodiments, the LLR buffer 102 can be programmed to store LLRs.For example, the LLR buffer 102 can store the LLR values for eachaggregation level. Alternatively or additionally, the LLR buffer 102 canreceive input from a processor (not shown in FIG. 1 ).

The de-rate matcher 104 receives input from the LLR buffer 102 accordingto the CCE location. Further, the de-rate matcher 104 receives inputfrom the blind decoding controller 110 (described below). Generally, thede-rate matcher 104 can receive instructions regarding which CCEs todecode from the blind decoding controller 110. Based on theinstructions, the de-rate matcher 104 can pull the LLR information forthe CCE from the LLR buffer 102. For example, the blind decodingcontroller 110 can indicate that a given set of CCEs can be decoded. Inresponse, the de-rate matcher 104 can pull the LLRs for the set of CCEs.

The decoder 106 can decode the CCE to retrieve the DCI. The decoder 106can receive inputs from the de-rate matcher 104 and the blind decodingcontroller 110. The decoder 106 can apply various decoding techniquesdepending on the technology that the receiver is using. For example, thedecoder 106 can use Viterbi decoding techniques for Long Term Evolution(LTE) and Polar decoding for new radio (NR). In some embodiments, thedecoder 106 can comprise multiple sub-decoders that each apply adifferent decoding technique. The decoder 106 can also include adecision module which is configured to stream CCEs to the appropriatesub-decoder. For instance, the decoder 106 can include a sub-decoderthat applies only Viterbi techniques and another sub-decoder thatapplies only Polar decoding techniques. The decision module can, then,based on the current technology, direct data to the appropriatesub-decoder.

The decoder 106 can output to the error detector 108. The error detector108 can determine if the decoded DCI is a valid DCI. To do so, the errordetector 108 can perform error detection on each result from the decoder106. The error detector 108 can apply known error checks such as cyclicredundancy checks (CRC), decoding metric checks, and/or DCI fieldvalidness checks. If the error detector 108 determines that the decodedDCI does have an error, the decoded DCI can be discarded. Upondiscarding, the error detector 108 can indicate to the blind decodingcontroller 120 that further decoded is required. In some cases, theerror detector 108 can move on the next decoded DCI. If the decoded DCIis valid, the error detector 108 can output the DCI for receipt byanother component of the receiver. In some embodiments, the valid DCIcan also be output to the blind decoding controller 110, as explainedbelow.

The blind decoding controller 110 can determine the order in which DCIhypothesis (with certain combination of CCEs) are decoded. In someembodiments, the blind decoding controller 110 can receive informationof a valid DCI from the error detector 108. The blind decodingcontroller 110 can use this information to skip other hypotheses thathave overlapping CCEs with the decoded DCI to avoid some overhead. Forinstance, the error detector 108 can inform the blind decodingcontroller 110 that a DCI from a given CCE(s) is valid. The blinddecoding controller 110, subsequently, can determine that anotherhypothesis overlaps the detected DCI. Based on this determination, theblind decoding controller 110 can instruct decoder 106 to skip decodingthe other hypothesis.

Further, the blind decoding controller 110 can receive input from theaggregation level predictor 112. The aggregation level predictor 112 canperform techniques to determine the probability of each aggregationlevel of the DCI to be detected. Based on the probability, theaggregation level predictor 112 can instruct the blind decodingcontroller 110 to provide instructions to the de-rate matcher 104 anddecoder 106 to decode CCE(s) based on the probabilities. For instance,the decoding can be done in descending order of probability. The CCE(s)with the aggregation level corresponding to the highest probability ofhaving the DCI can be decoded first. Subsequently, the CCEs with theaggregation level corresponding to the second highest can be decoded.The decoding can be done in this descending manner until the DCI isfound.

The probability of a DCI being transmitting with an aggregation levelcan depend on the packet error rate (PER). PER can be affected byfactors such as the signal to noise ratio (SNR) and/or the fadingmeasurements. In addition to the PER, the probability can be affected bythe reception history. Determining the probability based on SNR can bedependent on the principle that the (PER) and SNR are inverselyproportional. The transmitter (e.g., base station) can be aware of theterminal's SNR from terminal's channel state information feedback. Whenthe terminal's SNR is low, the transmitter (e.g., base station) sendsmultiple copies of the DCI. The transmitter does this to better ensurethat the receiver can piece together the entire signal, even whencertain packets are lost during transmission or includes incorrect bits.

Further, SNR is generally high when the distance between the transmitterand receiver is low. This is because it is less likely that noise caninterfere with a signal when the transmission distance is low. As such,based on the SNR a receiver can determine whether a PER is high or low.Based on the PER, subsequently, the receiver can also determine whethermultiple copies of the DCI were likely transmitted.

For example, if the transmitter has to transmit multiple copies, thetransmitter can use a higher aggregation level. As mentioned above,because each CCE contains 72 resource elements, the transmitter mayincrease the aggregation level when sending multiple copies (e.g., moresymbols). For instance, a base station can be far enough away from thereceiver that the base station can determine to send three copies of DCIinformation that is 90 bits. As such, the transmission includes 270 bitsof DCI information. The transmission can occur over a QPSK technology,which permits each resource element to include two bits. Thus, for 270bits, based on the capabilities of CCEs on QPSK, requires at least 135resource elements, which means at least 2 CCEs to transmit the threecopies of the DCI. Further, because at least 2 CCE's required, theaggregation level is at least two.

On the receiver side, the receiver may not be aware of the detailsmentioned above. However, the receiver can, for example, based on thedistance from the base station and, determine that the SNR is likely tobe low, which indicates that the PER is likely to the high. Further,rather than derive the SNR from other values (e.g., distance from thebase station), the receiver can include capabilities to measure the SNRof a given signal. For example, upon receipt, the receiver can measurethe SNR the signal and information the aggregation level predictor 112.

Due to the determination and/or SNR measurement, the aggregation levelpredictor 112 can predict the probability of each aggregation levelcarrying the DCI. In the example above, the aggregation level predictor112 can determine that aggregation level 4 has the highest probability,with aggregation levels 8, 2, and 1 begin the next highest probable, inorder. The aggregation level predictor 112 can instruct the blinddecoding controller 110 to decode CCE with the aggregation level 4first, then 8, then 2, and lastly, 1.

For example, the probability of an aggregation level carrying a DCIbased on PER can be calculated as follows. The target PER can be denotedas: per_(target). The SNR-to-PER relationship for each aggregation levelcan be determined based on curve p_(al)(snr). Once the receive measuresthe SNR value, the receiver map the measured SNR value on the curve todetermine the likely aggregation level of the signal corresponding tothe measured SNR value. The likely aggregation level can be denoted asper_(al)=p_(al)(snr). In this manner, the aggregation level with thelargest probability can be determined

${al}_{1} = {\arg{\min\limits_{al}( {{per}_{al}<={per}_{target}} )}}$

and the aggregation level can be ordered by descending probabilities asal₁, al₁*2, . . . , max AL, al₁/2, . . . , 1.

Another factor in determining the probability can be fadingmeasurements. The fading measurements can be affected by factors such asDoppler shift or delay spread. Similar to SNR, the relationship betweenPER and the fading measurements can help determine the probabilities ofeach aggregation level carrying the DCI. For example, the PER canincrease due to occurrences of Doppler shift. Thus, the aggregationlevel predictor 112 can determine higher probabilities for the higheraggregation levels.

In some embodiments, the aggregation level predictor 112 can use boththe fading measurements and the SNR measurement to determine aprobability. For instance, the aggregation level predictor 112 candetermine that a fading measurement can offset the SNR by a delta, Δ. ASNR-to-PER curve, p_(al)(snr), can be used to map the measured SNR inview of the delta as al per_(al)=p_(al)(snr−Δ). Subsequently theaggregation level with the highest probability can be determined as

${al}_{1} = {\arg{\min\limits_{al}( {{per}_{al}<={per}_{target}} )}}$

and the aggregation levels can be sorted based on probability as al₁,al₁*2, . . . , max AL, al₁/2, . . . , 1.

Yet another factor that can help determine the probability is thereception history of the receiver. The reception history can be based onthe communication between the receiver and a particular transmitter,based on all the communication that the receiver has received, based onthe distance between the receiver and the transmitter, or other suchcategories. In any case, the general principle being that theaggregation level predictor 112 can determine probabilities based on theprevious reception history of the terminal device. For example, if thereceiver has previously received 60% of the received DCI at aggregationlevel 4, and 20% at aggregation level 2, and another 20% at aggregationlevel 8, then the probabilities can reflect this reception history. Inanother example, the aggregation level predictor 112 can determine thatat the current between the receiver and transmitter at which the signalwas received, the receiver previously received DCI at aggregation level4 a majority of the time. Thus, the probabilities can reflect thisreception history.

In some embodiments, the reception history can be categorized based onthe SNR. For example, an SNR range can be divided into M+1 bins, as in:(−∞, SNR₁], (SNR₁, SNR₂], . . . , (SNR_(M−1), SNR_(M)], (SNR_(M−1), +∞).The SNR of a signal can be measured as “snr”. The “snr” value can thenbe placed in the right bin, where SNR_(m−1)<snr≤SNR_(m). From thisplacement, the aggregation level predictor 112 can determine theprobabilities.

In some embodiments, after the “snr” is placed in a bin, the aggregationlevel predictor 112 can determine whether the number of valid DCIcollected with SNR within the range of the bin surpasses a predeterminedthreshold value. If the number of valid DCI does surpass thepredetermined threshold, the aggregation level predictor 112 candetermine the probability based on the reception history. If the numberof valid DCI is lower than the predetermined threshold value, theaggregation level predictor 112 can apply one of the other techniquesmentioned above (e.g., fading measurement).

In this manner, the receiver can decode CCE is an order that is based onthe probability of DCI being transmitted with a given aggregation level.The probability can be based on the PER, which can be affected byfactors such as the SNR, fading measurements, and reception history.

Examples of Ordering CCEs

FIG. 2A illustrates an example 200 of control channel elements (CCEs)being received during a time period. The example 200 includes CCEs 206,208, 210, 212, 214, and 216 that are using corresponding aggregationlevels (ALs). In the example 200, the receiver can receive an AL2hypothesis with CCE 7 and CCE 8 first, then AL2 hypothesis with CCE 5and CCE 6, then an AL2 hypothesis with CCE 3 and CCE 4, then an AL2hypothesis with CCE 1 and CCE 2, then an AL4 hypothesis with CCEs 9-12,then an AL4 hypothesis with CCEs 1-4, and so on until all AL andcorresponding CCEs are received. In some embodiments, as in 206, a CCEcan be used in different hypotheses with different ALs. For example, areceiver can receive an AL2 hypothesis with CCE1-4, as in 210 and 212,and also with AL4, as in 206.

The time for receipt can range between T₀ and T_(N). FIGS. 2A-C aremerely examples to illustrate the technology described herein. Inparticular, an AL can indicate the number of CCEs that are allocated perPDCCH. For instance, an AL4 represents a group of 4 CCEs allocated to aPDCCH, as depicted in FIG. 2A. The relationship between AL and thenumber of CCEs is shown in Table 1 below:

TABLE 1 Aggregation Level (AL) Number of CCEs 1 1 2 2 4 4 8 8 16 16

FIG. 2B illustrates an example of the order 202 that the CCEs receivedin example 200 are decoded. In the order 202, a system (e.g., theaggregation level predictor 112) may have determined the probability foreach aggregation level and ordered the aggregation levels in descendingorder. The system can then instruct the decoder 218 to decode the CCEsbased on the aggregation level order. In FIG. 2B, the AL 2 is determinedto have a higher probability of carrying the DCI than AL 4. Thus, thedecoder 218 decodes the CCEs using AL 2 first.

In FIG. 2B, 210, 212, 214 and 216 are using AL 2. When multiple CCEs areusing the same aggregation levels, the decoder 218 and/or other system(e.g., aggregation level predictor 112) can determine that the CCEs canbe decoded in the order that they were received. Alternatively oradditionally, the system can perform additional checks on CCEs using thesame aggregation levels. For instance, the system can determine thefading channel for 214 and 216. Based on the fading channel, the orderin which they are decoded can be determined. In FIG. 2B, for example,CCE 216 may have been received first, and thus, can be decoded prior toCCE 214.

After the CCEs using AL 2, 210, 212, 214, and 216, the AL with nexthighest probability is AL 4. In FIG. 2B, CCEs 9-12 and 1-4 (208 and 206,respectively) are using AL 4. Thus, the logic described above can beapplied to determined which CCE can be decoded first.

In some embodiments, once the DCI is found, the remaining CCEs may notbe decoded. For instance, in FIG. 2B, if the DCI is found in the CCEs216, the remaining CCEs may not decoded. In some embodiments, thedetermination to continue decoding can depend on the aggregation level.For example, in AL 2, the system can know that the DCI is segmented overat least two CCEs. Thus, multiple CCEs using AL 2 can be decoded. InFIG. 2B, for example, if the DCI is found in the CCEs 216, the systemcan continue decoding CCEs using AL 2 until all hypotheses are decodedand obtained.

FIG. 2C illustrates another example 204 of the order that the CCEs aredecoded. In FIG. 2C, AL 4 has a higher probability than AL 2 of carryingthe DCI. Thus, the decoder 218 decodes the CCEs based on theprobability. Again, when multiple CCEs are using the same aggregationallevels, the system can determine, for example, which CCE was receivedfirst. Based on this determination, the CCEs using the same aggregationlevels can be decoded.

Example Methodology

FIG. 3 is a flowchart that illustrates a method 300 for decoding CCEs.The method 300 includes block 302, 304, and 306. Further, the method 300can optionally include block 308 and 310. The method 300 can be appliedby a terminal device such as mobile device (e.g., an iPhone) operatingon New Radio (NR) technology or Long Term Evolution (LTE) technology.The terminal device can include a receiver to receive signals from anetwork node such as base station. The terminal device can also includea processor to perform at least some of the techniques described herein. Further, at least some of the techniques described herein can beapplied upon receipt of signals in the downlink plane oftelecommunication systems, such as physical downlink control channel(PDCCH). In some embodiments, the method 300 can be applied at a networknode such as a based station. In this case, the method 300 includecausing the recipient of the PDCCH to determine the probability anddecode in an order indicative of the determined probability.

At block 302, a receiver (e.g., a terminal device) can receive a PDCCHsignal comprising a plurality of groups of CCEs. Each of the groups ofCCEs can be associated with an aggregation level of a plurality ofaggregation levels. The aggregation level can be the number of CCEs usedto transmit DCI. At block 304, the receiver can determine theprobability of the DCI being present at each aggregation level based onthe PER. The PER can be based on any of the SNR and/or the fadingmeasurement. Generally, the PER and SNR can be inversely related, andthe PER and the fading measurement can be directly related. The fadingmeasurement can be based on channel state information, for example,antenna correlation, Doppler shift, and other such measurements. Inaddition to PER, the probability can be based on the reception historyof the terminal and/or the transmission of the network node. Thereception history can be indicative of, for example, the aggregationlevel of previously received DCI.

Based on the determined probability, at block 306, the terminal candecode the groups of CCEs in an order. The order can reflect thedetermined probability of the DCI being present at each aggregationlevel. For example, the decoding can occur from the highest probabilityaggregation level to the lowest. In some cases, there may be more thanone CCE having the same aggregation level. In which case, the terminalcan decode the CCEs in the order that they were received by theterminal. In some cases, when the receiver determines the probability ofcertain aggregation level(s) to be zero, it can skip decoding for thoseaggregation level(s).

Further, based on the technology, the decoding can include applyingdifferent decoding schemes. For instance, if the technology is LTE, theterminal can apply Viterbi decoding algorithms. Alternatively, in NR,the terminal can apply Polar decoding algorithms. In either case, afterdecoding the CCEs, the terminal, at block 308, can determine whether theDCI is valid. To do so, the terminal can run an error check such as CRC.If the DCI is not valid, based on the error check, the terminal candiscard the DCI and proceed to decode the other CCEs or same CCEs with adifferent aggregation level, at block 310.

Example Computing System

FIG. 4 is a block diagram illustrating a diagrammatic representation ofa machine in the example form of a computer system operable to performaspects of the disclosed technology. For example, processing system 400may be an example implementation of a network node or terminal devicethat may implement the techniques introduced above. At least a portionof the processing system 400 may be included in an electronic device(e.g., a computer server) that supports one or more CPNs and/or one ormore UPNs. The processing system 400 may include one or more processors402, main memory 406, non-volatile memory 410, network adapter 412(e.g., network interfaces), display 418, input/output devices 420,control device 422 (e.g., keyboard and pointing devices), drive unit 424including a storage medium 426, and signal generation device 430 thatare communicatively connected to a bus 416. The bus 416 represents anyone or more separate physical buses, point to point connections, or anycombination thereof, connected by appropriate bridges, adapters, orcontrollers. The bus 416, therefore, can include, for example, a systembus, a Peripheral Component Interconnect (PCI) bus or PCI-Express bus, aHyperTransport or industry standard architecture (ISA) bus, a smallcomputer system interface (SCSI) bus, any version of a universal serialbus (USB), IIC (I2C) bus, or an Institute of Electrical and ElectronicsEngineers (IEEE) standard 1394 bus, also called “Firewire.” A bus mayalso be responsible for relaying data packets (e.g., via full or halfduplex wires) between components of a network appliance, such as aswitching engine, network port(s), tool port(s), etc.

In various embodiments, the processing system 400 operates as astandalone device, although the processing system 400 may be connected(e.g., wired or wirelessly) to other devices. For example, theprocessing system 400 may include a terminal that is coupled directly toa network appliance. As another example, the processing system 400 maybe wirelessly coupled to the network appliance.

In various embodiments, the processing system 400 may be a servercomputer, a client computer, a personal computer (PC), a user device, atablet PC, a laptop computer, a personal digital assistant (PDA), acellular telephone, an iPhone, an iPad, a Blackberry, a processor, atelephone, a web appliance, a network router, switch or bridge, aconsole, a hand-held console, a (hand-held) gaming device, a musicplayer, any portable, mobile, hand-held device, or any machine capableof executing a set of instructions (sequential or otherwise) thatspecify actions to be taken by the processing system 400.

While the main memory 406, non-volatile memory 410, and storage medium426 (also called a “machine-readable medium) are shown to be a singlemedium, the term “machine-readable medium” and “storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store one or more sets of instructions 428. The term“machine-readable medium” and “storage medium” shall also be taken toinclude any medium that is capable of storing, encoding, or carrying aset of instructions for execution by the processing system 400 and thatcause the processing system 400 to perform any one or more of themethodologies of the presently disclosed embodiments.

In general, the routines that are executed to implement the technologydisclosed above may be implemented as part of an operating system or anapplication, component, program, object, module, or sequence ofinstructions (collectively referred to as “computer programs”). Thecomputer programs typically comprise one or more instructions (e.g.,instructions 404, 408, 428) set at various times in various memory andstorage devices in a computer, and that, when read and executed by oneor more processing units or processors 402, cause the processing system400 to perform operations to execute elements involving the variousaspects of the above disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers, computer systems and/or other devices, thoseskilled in the art will appreciate that the various embodiments arecapable of being distributed as a program product in a variety of forms,and that the disclosure applies equally regardless of the particulartype of machine or computer-readable media used to actually effect thedistribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include recordable typemedia such as volatile and non-volatile memory devices 410, floppy andother removable disks, hard disk drives, optical disks (e.g., CompactDisk Read-Only Memory (CD ROMS), Digital Versatile Disks (DVDs)), andtransmission type media such as digital and analog communication links.

The network adapter 412 enables the processing system 400 to mediatedata in a network 414 with an entity that is external to the processingsystem 400, such as a network appliance, through any known and/orconvenient communications protocol supported by the processing system400 and the external entity. The network adapter 412 can include one ormore of a network adaptor card, a wireless network interface card, arouter, an access point, a wireless router, a switch, a multilayerswitch, a protocol converter, a gateway, a bridge, bridge router, a hub,a digital media receiver, and/or a repeater.

The network adapter 412 can include a firewall which can, in someembodiments, govern and/or manage permission to access/proxy data in acomputer network, and track varying levels of trust between differentmachines and/or applications. The firewall can be any number of moduleshaving any combination of hardware and/or software components able toenforce a predetermined set of access rights between a particular set ofmachines and applications, machines and machines, and/or applicationsand applications, for example, to regulate the flow of traffic andresource sharing between these varying entities. The firewall mayadditionally manage and/or have access to an access control list whichdetails permissions including for example, the access and operationrights of an object by an individual, a machine, and/or an application,and the circumstances under which the permission rights stand.

Other network security functions can be performed or included in thefunctions of the firewall, including intrusion prevention, intrusiondetection, next-generation firewall, personal firewall, etc.

As indicated above, the techniques introduced here implemented by, forexample, programmable circuitry (e.g., one or more microprocessors),programmed with software and/or firmware, entirely in special-purposehardwired (i.e., non-programmable) circuitry, or in a combination ofsuch forms. Special-purpose circuitry can be in the form of, forexample, one or more application-specific integrated circuits (ASICs),programmable logic devices (PLDs), field-programmable gate arrays(FPGAs), etc.

Note that any of the embodiments described above can be combined withanother embodiment, except to the extent that it may be stated otherwiseabove or to the extent that any such embodiments might be mutuallyexclusive in function and/or structure.

CONCLUSION

The embodiments set forth herein represent the necessary information toenable those skilled in the art to practice the embodiments andillustrate the best mode of practicing the embodiments. Upon reading thedescription in light of the accompanying figures, those skilled in theart will understand the concepts of the disclosure and will recognizeapplications of these concepts that are not particularly addressedherein. These concepts and applications fall within the scope of thedisclosure and the accompanying claims.

The above description and drawings are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known details are not described in order to avoidobscuring the description. Further, various modifications may be madewithout deviating from the scope of the embodiments.

As used herein, unless specifically stated otherwise, terms such as“processing,” “computing,” “calculating,” “determining,” “generating,”or the like, refer to actions and processes of a computer or similarelectronic computing device that manipulates and transforms datarepresented as physical (electronic) quantities within the computer'smemory or registers into other data similarly represented as physicalquantities within the computer's memory, registers, or other suchstorage medium, transmission, or display devices.

Reference herein to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment of thedisclosure. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Moreover, various features are describedwhich may be exhibited by some embodiments and not by others. Similarly,various requirements are described which may be requirements for someembodiments but not for other embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed above, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. For convenience, certainterms may be highlighted, for example using italics and/or quotationmarks. The use of highlighting has no influence on the scope and meaningof a term; the scope and meaning of a term is the same, in the samecontext, whether or not it is highlighted. It will be appreciated thatthe same thing can be said in more than one way.

Consequently, alternative language and synonyms may be used for any oneor more of the terms discussed herein, nor is any special significanceto be placed upon whether or not a term is elaborated or discussedherein. Synonyms for certain terms are provided. A recital of one ormore synonyms does not exclude the use of other synonyms. The use ofexamples anywhere in this specification including examples of any termdiscussed herein is illustrative only and is not intended to furtherlimit the scope and meaning of the disclosure or of any exemplifiedterm. Likewise, the disclosure is not limited to various embodimentsgiven in this specification.

Without intent to further limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given above. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which this disclosure pertains. In the case of conflict, thepresent document, including definitions will control.

From the foregoing, it will be appreciated that specific embodiments ofthe invention have been described herein for purposes of illustration,but that various modifications may be made without deviating from thescope of the invention. Accordingly, the invention is not limited exceptas by the appended claims.

What is claimed is:
 1. A method for reducing latency, comprising:receiving, at a terminal, a physical downlink control channel (PDCCH)signal, the PDCCH signal comprising a plurality of groups of controlchannel elements (CCEs), wherein each group of CCEs is associated withan aggregation level of a plurality of aggregation levels; determining,by the terminal, based on a packet error rate (PER), a probability ofdownlink control information (DCI) being present at each aggregationlevel of the plurality of aggregation levels; and decoding, by theterminal, the groups of CCEs in an order, wherein the order is based onthe probability of the DCI being present at each aggregation level. 2.The method of claim 1, wherein the PER is based on any of the signal tonoise ratio (SNR) of the PDCCH signal, a fading measurement of the PDCCHsignal, or any combination thereof.
 3. The method of claim 2, whereinthe PER and the SNR are inversely related, and wherein the PER and thefading measurement are directly related.
 4. The method of claim 2,wherein the fading measurement is based on any of a delay caused byDoppler shift, frequency selectivity, or any combination thereof.
 5. Themethod of claim 1, wherein the probability is further based on areception history of the terminal.
 6. The method of claim 5, wherein thereception history is indicative of an aggregation level of previouslyreceived DCI.
 7. The method of claim 1, wherein the order is from ahighest probability to a lowest probability.
 8. The method of claim 1,wherein one or more CCEs are associated with the same aggregation level,the method further comprising: decoding the one or more CCEs based on asequence in which the one or more CCEs were received by the terminal. 9.The method of claim 1, wherein the aggregation level is indicative of anumber of CCEs used to transmit the DCI.
 10. The method of claim 1,wherein the terminal is operating in Long Term Evolution (LTE)technology, and wherein decoding the plurality of CCEs furthercomprising: applying Viterbi decoding algorithms.
 11. The method ofclaim 1, wherein the terminal is operating New Radio (NR) technology,and wherein decoding the plurality of CCEs further comprising: applyingPolar decoding algorithms.
 12. The method of claim 1, furthercomprising: in response to decoding a given CCE including the DCI,determining whether the DCI is valid based on results of an error check.13. The method of claim 12, wherein performing the error checkcomprises: performing a cyclic redundancy check (CRC) to detect changesin the DCI.
 14. The method of claim 12, further comprising: in responseto determining that the DCI is not valid, discarding the DCI anddecoding remaining CCEs of the plurality of CCEs or decoding the givenCCE with a different aggregation level.
 15. A method for reducinglatency, comprising: transmitting, by a network node, a physicaldownlink control channel (PDCCH) signal, the PDCCH signal comprising aplurality of groups of control channel elements (CCEs), wherein eachgroup of CCEs is associated with an aggregation level of a plurality ofaggregation levels; causing, by the network node, based on a packeterror rate (PER), determination of a probability of downlink controlinformation (DCI) being present at each aggregation level of theplurality of aggregation levels; and causing, by the network node,decoding of the groups of CCEs in an order, wherein the order is basedon the probability of the DCI being present at each aggregation level.16. The method of claim 15, wherein the PER is based on any of thesignal to noise ratio (SNR) of the PDCCH signal, a fading measurement ofthe PDCCH signal, or any combination thereof.
 17. The method of claim15, wherein the probability is further based on the transmission historyof the network node.
 18. The method of claim 15, wherein the order isfrom a highest probability to a lowest probability.
 19. The method ofclaim 15, wherein the PDCCH signal is transmitted to a terminal device.20. A system comprising: a receiver through which to receive, a physicaldownlink control channel (PDCCH) signal, the PDCCH signal comprising aplurality of groups of control channel elements (CCEs), wherein eachgroup of CCEs is associated with an aggregation level of a plurality ofaggregation levels; a processor configured to: determine, based on apacket error rate (PER), a probability of downlink control information(DCI) being present at each aggregation level of the plurality ofaggregation levels; and decode the groups of CCEs in an order, whereinthe order is based on the probability of the DCI being present at eachaggregation level.